有没有使用 VTK 将 DICOM（ct 扫描）图像转换为点云的方法？
VTK 允许读取 DICOM 和 DICOM 系列以及体积渲染，但是否可以从一系列 DICOM 图像生成点云？
如果在 VTK 中不可能，是否有其他库可以用于此目的？
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这是一个dicom到点云的演示。Dicom 文件的变化很大，具体取决于图像的收集方式，但这是我们一段时间以来一直用于 CT 扫描的文件。这是“手动版本”，即您需要与终端交互以导航 dicom 目录。可以自动执行此操作，但高度依赖于您的应用程序。
我安装了 pcl 8.0 和 vtkdicom。（我能够在没有 vtkdicom 的情况下进行有限的实现，但它的功能使应用程序在处理各种 dicom 目录结构时更加健壮）。
您需要将 main 中的函数指向计算机上的相应目录（应该是包含 DICOMDIR 文件的文件）。加载 dicom 后，可视化器具有键盘输入 m 和 n 来控制要可视化的强度目标。（您可以轻松更改代码以过滤任何参数：x、y、z、强度）并且可以根据需要更改宽度或步长。
#include <pcl/common/common_headers.h>
#include <pcl/visualization/pcl_visualizer.h>
#include <pcl/filters/passthrough.h>
#include <boost/thread/thread.hpp>
#include <vtkSmartPointer.h>
#include <vtkDICOMImageReader.h>
#include "vtkImageData.h"
#include "vtkDICOMDirectory.h"
#include "vtkDICOMItem.h"
#include "vtkStringArray.h"
#include "vtkIntArray.h"
#include "vtkDICOMReader.h"
bool loadDICOM(pcl::PointCloud<pcl::PointXYZI>::Ptr outCloud, std::string fullPathToDicomDir)
{
// load DICOM dir file
vtkSmartPointer<vtkDICOMDirectory> ddir =
vtkSmartPointer<vtkDICOMDirectory>::New();
ddir->SetDirectoryName(fullPathToDicomDir.c_str());
ddir->Update();
//select patient
int n = ddir->GetNumberOfPatients();
int patientSelection = 0;
if (n > 1)
{
std::cout << "Select Patient number, total count: " << n << std::endl;
std::string userInput;
std::getline(std::cin, userInput);
patientSelection = std::stoi(userInput);
}
const vtkDICOMItem& patientItem = ddir->GetPatientRecord(patientSelection);
std::cout << "Patient " << patientSelection << ": " << patientItem.Get(DC::PatientID).AsString() << "\n";
//select study
vtkIntArray* studies = ddir->GetStudiesForPatient(patientSelection);
vtkIdType m = studies->GetMaxId() + 1;
int studySelection = 0;
if (m > 1)
{
std::cout << "Select study, total count: " << m << std::endl;
std::string userInput;
std::getline(std::cin, userInput);
studySelection = std::stoi(userInput);
}
int j = studies->GetValue(studySelection);
const vtkDICOMItem& studyItem = ddir->GetStudyRecord(j);
const vtkDICOMItem& studyPItem = ddir->GetPatientRecordForStudy(j);
cout << " Study " << j << ": \""
<< studyItem.Get(DC::StudyDescription).AsString() << "\" \""
<< studyPItem.Get(DC::PatientName).AsString() << "\" "
<< studyItem.Get(DC::StudyDate).AsString() << "\n";
int k0 = ddir->GetFirstSeriesForStudy(j);
int k1 = ddir->GetLastSeriesForStudy(j);
int seriesSelection;
std::cout << "Select series, range: " << k0 << " to " << k1 << std::endl;
for (int i = k0; i <= k1; i++)
{
const vtkDICOMItem& seriesItem = ddir->GetSeriesRecord(i);
vtkStringArray* a = ddir->GetFileNamesForSeries(i);
cout << " Series " << i << ": \""
<< seriesItem.Get(DC::SeriesDescription).AsString() << "\" "
<< seriesItem.Get(DC::SeriesNumber).AsString() << " "
<< seriesItem.Get(DC::Modality).AsString() << ", Images: "
<< a->GetNumberOfTuples() << "\n";
}
std::string userInput;
std::getline(std::cin, userInput);
seriesSelection = std::stoi(userInput);
const vtkDICOMItem& seriesItem = ddir->GetSeriesRecord(seriesSelection);
cout << " Series " << seriesSelection << ": \""
<< seriesItem.Get(DC::SeriesDescription).AsString() << "\" "
<< seriesItem.Get(DC::SeriesNumber).AsString() << " "
<< seriesItem.Get(DC::Modality).AsString() << "\n";
vtkStringArray* a = ddir->GetFileNamesForSeries(seriesSelection);
vtkDICOMReader* reader = vtkDICOMReader::New();
reader->SetFileNames(a);
reader->Update();
vtkSmartPointer<vtkImageData> sliceData = reader->GetOutput();
int numberOfDims = sliceData->GetDataDimension();
int* dims = sliceData->GetDimensions();
std::cout << "Cloud dimensions: ";
int totalPoints = 1;
for (int i = 0; i < numberOfDims; i++)
{
std::cout << dims[i] << " , ";
totalPoints = totalPoints * dims[i];
}
std::cout << std::endl;
std::cout << "Number of dicom points: " << totalPoints << std::endl;
//read data into grayCloud
double* dataRange = sliceData->GetScalarRange();
double* spacingData = reader->GetDataSpacing();
std::cout << "Data intensity bounds... min: " << dataRange[0] << ", max: " << dataRange[1] << std::endl;
if (numberOfDims != 3)
{
std::cout << "Incorrect number of dimensions in dicom file, generation failed..." << std::endl;
return false;
}
else
{
Eigen::RowVector3f spacing = Eigen::RowVector3f(spacingData[0], spacingData[1], spacingData[2]);
Eigen::RowVector3i dimensions = Eigen::RowVector3i(dims[0], dims[1], dims[2]);
outCloud->points.clear();
std::cout << "x spacing: " << spacing(0) << std::endl;
std::cout << "y spacing: " << spacing(1) << std::endl;
std::cout << "z spacing: " << spacing(2) << std::endl;
for (int z = 0; z < dims[2]; z++)
{
if (z % 50 == 0)
{
double percentageComplete = (double)z / (double)dims[2];
std::cout << "Dicom Read Progress: " << (int)(100.0 * percentageComplete) << "%" << std::endl;
}
for (int y = 0; y < dims[1]; y++)
{
for (int x = 0; x < dims[0]; x++)
{
double tempIntensity = sliceData->GetScalarComponentAsDouble(x, y, z, 0);
int tempX = x;
pcl::PointXYZI tempPt = pcl::PointXYZI();
if (!isinf(tempIntensity) && !isnan(tempIntensity))
{
//map value into positive realm
//tempIntensity = ((tempIntensity - dataRange[0]) / (dataRange[1] - dataRange[0]));
if (tempIntensity > SHRT_MAX) { tempIntensity = SHRT_MAX; }
else if (tempIntensity < SHRT_MIN) { tempIntensity = SHRT_MIN; }
}
else
{
tempIntensity = 0;
}
tempPt.x = tempX;
tempPt.y = y;
tempPt.z = z;
tempPt.intensity = tempIntensity;
outCloud->points.push_back(tempPt);
}
}
}
}
std::cout << "Load Dicom Cloud Complete!" << std::endl;
return true;
}
int indexSlice = 0;
void keyboardEventOccurred(const pcl::visualization::KeyboardEvent& event, void* viewer)
{
if (event.getKeySym() == "n" && event.keyDown())
{
indexSlice -= 1;
}
else if (event.getKeySym() == "m" && event.keyDown())
{
indexSlice += 1;
}
}
void displayCloud(pcl::PointCloud<pcl::PointXYZI>::Ptr cloud, std::string field, int step, int width, std::string window_name = "default")
{
boost::shared_ptr<pcl::visualization::PCLVisualizer> viewer(new pcl::visualization::PCLVisualizer(window_name));
viewer->setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 2, "id");
viewer->registerKeyboardCallback(keyboardEventOccurred, (void*)viewer.get());
pcl::PointCloud<pcl::PointXYZI>::Ptr tempCloud(new pcl::PointCloud<pcl::PointXYZI>);
pcl::PassThrough<pcl::PointXYZI> pass;
pass.setInputCloud(cloud);
pass.setFilterFieldName(field); //could gate this on intensity if u preferred
int lastIndex = indexSlice-1; //proc first cycle
while (!viewer->wasStopped()) {
if (indexSlice != lastIndex)
{
int low = step * indexSlice - width / 2;
int high = step * indexSlice + width / 2;
pass.setFilterLimits(low, high);
pass.filter(*tempCloud);
lastIndex = indexSlice;
std::cout << field<< " range: " <<low<<" , "<<high<< std::endl;
viewer->removeAllPointClouds();
pcl::visualization::PointCloudColorHandlerGenericField<pcl::PointXYZI> point_cloud_color_handler(tempCloud, "intensity");
viewer->addPointCloud< pcl::PointXYZI >(tempCloud, point_cloud_color_handler, "id");
}
viewer->spinOnce(50);
}
viewer->close();
}
// --------------
// -----Main-----
// --------------
int main(int argc, char** argv)
{
pcl::PointCloud<pcl::PointXYZI>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZI>);
loadDICOM(cloud, "C:/Local Software/voyDICOM/resources/DICOM_Samples/2021APR14 MiniAchors_V0");
displayCloud(cloud,"intensity",100,50);
return 0;
}
请注意，在大多数情况下，dicom 文件的原始尺寸相对较大，因此我很少（从不？）将整个 dicom 文件加载到点云中（直到此代码）。通常我所做的是以密集格式（短数组）处理它，然后根据从该数据中选择的内容创建云。通过这种方式，您可以在进入稀疏数据集（点云）之前进行某些从锁定数据网格（打开、关闭等）中受益的成像操作，因为这里一切都变得更加昂贵。
它与我的一个调试 dicom 集一起使用的漂亮图片：
于 2018-01-18T22:30:13.443 回答
-2
毕竟，我想我可能已经找到了一种方法。还没有尝试过，但理论上它应该可以工作。
首先，DICOM 图像需要使用 VTK 转换为 .vtk 格式，一旦将 DICOM 图像转换为 .vtk，就可以使用 PCL（点云库）将它们转换为 .pcd（点云格式）。
于 2017-10-23T14:37:18.923 回答